A New Water Level Measurement Technique Using Artificial Intelligent
Flash floods are a growing concern worldwide, causing economic and social losses, increased death rates, and damage to infrastructure. The rapid nature of these disasters has led to delayed and inaccurate flood event information, causing public confusion and delays in response. This study aims t...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2025
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12530/1/P17889_e2ab609917f705c900ef134d8bff8cfc.pdf http://eprints.uthm.edu.my/12530/ |
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| Summary: | Flash floods are a growing concern worldwide, causing economic and
social losses, increased death rates, and damage to infrastructure. The
rapid nature of these disasters has led to delayed and inaccurate flood
event information, causing public confusion and delays in response. This
study aims to use AI to measure flood levels in real-time to improve flood
information during flash floods. In this study, an Axia automobile as a
model has been tested in an open space area. Then, box and manilla card
is used as a level to mark the height of flood water, which is 15cm, 30cm,
and up to 105cm. Data was collected by taking pictures of the vehicle
from a distance of 620cm, 720cm, and 820cm. Teachable Machine
applications has been used in this experiment to train the model for the
data analysis. Image processing methods from the data have been used
to identify flood elevation. Key findings show the true percentages and
false percentages accuracy of AI measurements on water level and
distances measurement. Accuracy of AI measurements for distance
represent 80% accuracy for correct value and 20% for the wrong values.
Other than that, for accuracy of AI measurements on water level shows
90.5% indicates the accurate percentage and 9.5% indicates the
inaccurate percentages. Additionally, the comparison in measuring water
level between two devices, which is camera and Iphone show that the
camera achieves 87% is accurate meanwhile the Iphone reached 62% of
accurate values. Good agreement shows based on findings. However,
some areas need to be improved especially for Iphone devices. |
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